Fabrication of poly- and single-crystalline platinum nanostructures using hole-mask colloidal lithography, electrodeposition and annealing
Bibliographic record
Abstract
Colloidal lithography (CL) is a generic name for a collection of nanolithographic techniques, based on using colloidal nanoparticles as pattern (mask)-defining entities to produce various nanostructures. A key step in CL processes is the deposition, usually by evaporation or sputtering, of the material that makes up the final nanostructures. We have for the first time combined a special version of CL, called hole-mask colloidal lithography (HCL), with electrodeposition. We demonstrate how electrodeposition of Pt onto Au and carbon substrates, through a lithographic mask, can be used to prepare well-defined nanostructured surfaces. The results are compared with evaporated structures and characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and cyclic voltammetry. Specific results are: (i) electrodeposition generates structures with very good adhesion, (ii) due to differences in the deposition mechanism, structures with much larger aspect (height/width) ratio can be made with electrodeposition than with evaporation and (iii) the originally deposited polycrystalline nanoparticles can be annealed into single crystals, as demonstrated by electron diffraction, SEM and TEM, before and after annealing, which is of great value for fundamental (electro)catalysis studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".